Seismic interpretation is a process that aims to investigate the earth subsurface in order to collect relevant information for analysis. The earth subsurface consists of material layers with distinct acoustic impedances, due to the densities of the minerals of rock matrix and porosity characteristics. The interfaces between material layers are called horizons, which are the basic structure for seismic interpretation. Horizons may be analyzed and indicate the existence of several features, such as faults, anticline and monocline folds and salt bodies. Faults are generally sub-vertical fractures across which there is a measurable displacement of rock layers. They are recognized by horizon discontinuities and usually interpreted as straight lines or connected line segments. Faults generally affect fluid mobility in the reservoir. In some cases, they seal the porous reservoir rock by deforming and juxtaposing rock layers and lead to the formation of hydrocarbon reservoirs or allow the hydrocarbon to migrate to a porous rock layer, which will become the reservoir. Therefore, the identification of faults and related structures is very important to recognize potential regions where hydrocarbons can be found.
A seismic dataset comprises numerous closely-spaced seismic traces. Each seismic trace represents a sequence of floating-point values containing the amplitude of the elastic waves reflected by geologic bodies beneath the surface. Each sample of the seismic data is related to the time that the reflected energy in some subsurface structure took to travel the path between the energy source and the receiver (geophone or hydrophone). After some processing, these samples can be presented in depth, rather than time. A seismic dataset can be presented as pre-stack or post-stack data. Pre-stack data corresponds to the raw data, which presents noise and contains the full wave information. Post-stack data, in turn, represents the preprocessed data, with more consolidated data and noise reduction. Despite the noise reduction efforts, it remains extremely difficult to completely eliminate the effect of noise on data, thus the quality of seismic data is rarely very high. This fact directly affects the accuracy of the interpretation results and the reliability of most automatic methods because of the high quality data required. Seismic datasets can be several terabytes in size after processing, implying that it takes longer to build trusted models for reaching exploration decisions.
The fault interpretation procedure, a very important stage of seismic interpretation, is still a very time-consuming and labor-intensive task. Interpreters often need to define a few hundreds, up to several thousands, of points within a seismic dataset in order to track the accurate spatial position of faults. Therefore, a detailed interpretation in a large seismic dataset is almost impossible to perform in a reasonable timeframe.
A need therefore exists for methods and apparatus that automatically or semi-automatically identify seismic fault candidates in large seismic datasets. A further need exists for techniques that identify and rank seismic fault candidates in seismic datasets, even in the presence of noise in the datasets.